使用opnalpr训练目标检测级联分类器

1.环境

windows7 , opencv3.0 , python-2.6 , PIL-1.1.6.win32-py2.6.exe

2.安装python2.6和PIL

3.下载openalpr的train-detector-master

3.修改prep.py文件，可以直接复制以下代码，修改OPENCV_DIR和BASE_DIR为你自己的目录即可

#!/usr/bin/pythonimport osimport Imageimport uuidimport shutilimport sys#修改成你需要检测目标的宽度和高度WIDTH=136HEIGHT=36#这个目录里面需要放你的正样本COUNTRY='china'#WIDTH=52#HEIGHT=13#COUNTRY='eu'#constants，需要填写你的opencv安装目录，注意这里制定x64位，使用32位可能在训练的时候回出现分配内存不足的情况OPENCV_DIR= 'D:\\software\\opencv\\opencv_300\\opencv\\build\\x64\\vc11\\bin'SAMPLE_CREATOR = OPENCV_DIR + '\\opencv_createsamples'#指定prep.py文件所在的目录即可BASE_DIR            = 'E:\\PlateRec\\train-detector-master\\train-detector-master\\'#训练的模型输出目录OUTPUT_DIR          = BASE_DIR + "out\\"#负样本放置目录INPUT_NEGATIVE_DIR  = BASE_DIR + 'raw-neg\\'#正样本放置目录INPUT_POSITIVE_DIR  = BASE_DIR + COUNTRY + '\\'#初始状态目录为空，在运行prep.py neg后，这个目录会有内容OUTPUT_NEGATIVE_DIR  = BASE_DIR + 'negative\\'OUTPUT_POSITIVE_DIR  = BASE_DIR + 'positive\\'POSITIVE_INFO_FILE  = OUTPUT_POSITIVE_DIR + 'positive.txt'NEGATIVE_INFO_FILE  = OUTPUT_NEGATIVE_DIR + 'negative.txt'VEC_FILE            = OUTPUT_POSITIVE_DIR + 'vecfile.vec'vector_arg = '-vec %s' % (VEC_FILE)width_height_arg = '-w %d -h %d' % (WIDTH, HEIGHT)def print_usage():    print ("Usage: prep.py [Operation]")    print ("   -- Operations --")    print ("  neg        -- Prepares the negative samples list")    print ("  pos        -- Copies all the raw positive files to a opencv vector")    print ("  showpos    -- Shows the positive samples that were created")    print ("  train      -- Outputs the command for the Cascade Training algorithm")def file_len(fname):    with open(fname) as f:        for i, l in enumerate(f):            pass    return i + 1command=""if command != "":    passelif len(sys.argv) != 2:    print_usage()    exit()else:    command = sys.argv[1]if command == "neg":    print ("Neg")    # Get rid of any spaces    for neg_file in os.listdir(INPUT_NEGATIVE_DIR):        if " " in neg_file:            fileName, fileExtension = os.path.splitext(neg_file)            newfilename =  str(uuid.uuid4()) + fileExtension            #print "renaming: " + files + " to "+ root_dir + "/" + str(uuid.uuid4()) + fileExtension            os.rename(INPUT_NEGATIVE_DIR + neg_file, INPUT_POSITIVE_DIR + newfilename)    f = open(NEGATIVE_INFO_FILE,'w')    ## Write a list of all the negative files    for neg_file in os.listdir(INPUT_NEGATIVE_DIR):        if os.path.isdir(INPUT_NEGATIVE_DIR + neg_file):            continue        shutil.copy2(INPUT_NEGATIVE_DIR + neg_file, OUTPUT_NEGATIVE_DIR + neg_file )        f.write(OUTPUT_NEGATIVE_DIR + neg_file + "\n")    f.close()elif command == "pos":    print ("Pos")    info_arg = '-info %s' % (POSITIVE_INFO_FILE)    # Copy all files in the raw directory and build an info file    ## Remove all files in the output positive directory    for old_file in os.listdir(OUTPUT_POSITIVE_DIR):        os.unlink(OUTPUT_POSITIVE_DIR + old_file)    ## First, prep the sample filenames (make sure they have no spaces)    for files in os.listdir(INPUT_POSITIVE_DIR):        if os.path.isdir(INPUT_POSITIVE_DIR + files):            continue        # Rename the file if it has a space in it        newfilename = files        if " " in files:            fileName, fileExtension = os.path.splitext(files)            newfilename =  str(uuid.uuid4()) + fileExtension            #print "renaming: " + files + " to "+ root_dir + "/" + str(uuid.uuid4()) + fileExtension            os.rename(INPUT_POSITIVE_DIR + files, INPUT_POSITIVE_DIR + newfilename)# Copy from the raw directory to the positive directory        shutil.copy2(INPUT_POSITIVE_DIR + newfilename, OUTPUT_POSITIVE_DIR + newfilename )    total_pics = 0    ## Create the positive.txt input file    f = open(POSITIVE_INFO_FILE,'w')    for filename in os.listdir(OUTPUT_POSITIVE_DIR):        if os.path.isdir(OUTPUT_POSITIVE_DIR + filename):            continue        if filename.endswith(".txt"):            continue        try:            img = Image.open(OUTPUT_POSITIVE_DIR + filename)            # get the image's width and height in pixels            width, height = img.size            f.write(filename + " 1 0 0 " + str(width) + " " + str(height) + '\n')            total_pics = total_pics + 1        except IOError:            print ("Exception reading image file: " + filename)    f.close()    # Collapse the samples into a vector file    execStr = '%s\\opencv_createsamples %s %s %s -num %d' % (OPENCV_DIR, vector_arg, width_height_arg, info_arg, total_pics )    print (execStr)    os.system(execStr)    #opencv_createsamples -info ./positive.txt -vec ../positive/vecfile.vec -w 120 -h 60 -bg ../negative/PentagonCityParkingGarage21.jpg -num 100elif command == "showpos":    print ("SHOW")    execStr = '%s\\opencv_createsamples -vec %s -w %d -h %d' % (OPENCV_DIR, VEC_FILE, WIDTH, HEIGHT )    print (execStr)    os.system(execStr)    #opencv_createsamples -vec ../positive/vecfile.vec -w 120 -h 60elif command == "train":    print ("TRAIN")    data_arg = '-data %s\\' % (OUTPUT_DIR)    bg_arg = '-bg %s' % (NEGATIVE_INFO_FILE)    try:        num_pos_samples = file_len(POSITIVE_INFO_FILE)    except:    num_pos_samples = -1    num_neg_samples = file_len(NEGATIVE_INFO_FILE)    execStr = '%s\\opencv_traincascade %s %s %s %s -numPos %d -numNeg %d -maxFalseAlarmRate 0.45 -featureType LBP -numStages 13' % (OPENCV_DIR, data_arg, vector_arg, bg_arg, width_height_arg, num_pos_samples, num_neg_samples )    print ("Execute the following command to start training:")    print (execStr)#D:\software\opencv\opencv_300\opencv\build\x64\vc11\bin\opencv_traincascade -data E:\PlateRec\train-detector-master\train-detector-master\out\ -vec E:\PlateRec\train-detector-master\train-detector-master\positive\vecfile.vec -bg E:\PlateRec\train-detector-master\train-detector-master\negative\negative.txt -w 136 -h 36 -numPos 1600 -numNeg 6000 -minHitRate 0.99 -maxFalseAlarmRate 0.45 -featureType LBP -numStages 20    #opencv_traincascade -data ./out/ -vec ./positive/vecfile.vec -bg ./negative/negative.txt -w 120 -h 60 -numPos 99 -numNeg 5  -featureType LBP -numStages 20elif command == "SDFLSDFSDFSDF":    root_dir = '/home/mhill/projects/anpr/AlprPlus/samples/svm/raw-pos'    outputfilename = "positive.txt"else:    print_usage()    exit()

4.将正样本放在INPUT_POSITIVE_DIR中，负样本放在raw-neg目录中

5.执行以下命令

prep.py neg

prep.py pos

D:\software\opencv\opencv_300\opencv\build\x64\vc11\bin\opencv_traincascade -data E:\PlateRec\train-detector-master\train-detector-master\out\ -vec E:\PlateRec\train-detector-master\train-detector-master\positive\vecfile.vec -bg E:\PlateRec\train-detector-master\train-detector-master\negative\negative.txt -w 45 -h 15 -numPos 1500 -numNeg 4500 -minHitRate 0.99 -maxFalseAlarmRate 0.45 -featureType LBP -numStages 16 -precalcValBufSize 5 -precalcIdxBufSize 100

注意第三个命令很关键，第一个路径是你安装的opencv包含opencv_traincascade.exe文件的目录

-minHitRate 表示每个分类器最多有1600*(1-0.99)个正样本被分类成负样本，下级取正样本的时候需要多取这么多正样本

-maxFalseAlarmRate 表示最大虚警率，每级，最多将6000*0.45个样本分类成正样本

-data 后面是模型输出目录

-vec 后面是执行prep.py pos后生成的vecfile.vec文件目录，其实就在positive目录中

-bg 后面是执行prep.py neg生成negative.txt文件目录，其实就是在negative目录中

-numPos 表示每个弱分类器使用的正样本数，而不是你拥有的正样本的总数

计算公式：正样本总数 >= (numPos + (numStages-1) * (1 - minHitRate) * numPos)

-numNeg 同样表示的是每个弱分类器使用的负样本数，但是这个值可以直接使用你负样本的总个数

-precalcValBufSize 表示每个样本提取的特征使用多大单位为MB,如果不设置默认为1024MB也就是1G，这很大，可能会导致训练到某一stage的时候分配空间失败，所以建议使用小一点的值我这里使用的LBP特征

-precalcIdxBufSize 表示什么索引的buf大小，据说这个值越大训练的越快，但是我为了保险起见别训练个十几个小时又分配内存不足，所以使用的是100MB，默认也是1024MB

ps:我使用1925个正样本，4706个负样本，发现使用上面这个命令参数训练没有问题，而且训练出来的检测效果不错，可能正样本更多一些，比如倾斜着的车牌，负样本更丰富一些比如栅栏，栏杆等容易被识别成车牌的加入到负样本中，效果会更好一些。注意那个w和h不一定就是你的正样本的宽度和高度，可以是等比例缩小的，实际上我试过就是样本136*36这么大的，识别不到目标。

6.开始训练之后如下图所示